Failures in pre-clinical drug development are largely attributable to poor pharmacokinetics and toxicity. Successful drug development hence requires good model systems whose results can be reliably extrapolated to humans in the clinic. Recently, we have developed predictive QSAR models based on advanced machine learning algorithms that perform fairly well in the prediction of pharmacokinetic properties. However, QSAR based models are limited in their ability to generalise out of the training-set chemical space. Some in vitro methods provide reasonable correlations with in vivo pharmacokinetics, but important exceptions such as the substituted anthracyclines, where large variability in properties result from simple substitutions, stress the need for improved testing methods. Over the last decade there has been a steady improvement in pharmacokinetic prediction leading to reduction in failures.
The post-marketing withdrawal of drugs is often due to their toxicity. It is observed that toxicity is often associated with biotransformation. This is carried out mainly by a family of enzymes called cytochrome P450s that exhibits great inter-species variations and has also been associated with the development of idiosyncratic drug toxicity in humans. Due to the abundance of these enzymes in the liver and due to its portal location in the body, drug-induced liver injury (DILI) is a major cause for drug withdrawal. Since about half of the liver failures are due to drug exposure, methods that enable reliable prediction of DILI are critical. However, injury to the liver in humans cannot be extrapolated from toxicity in rodents due to metabolic differences. In preclinical development, hepatic intrinsic clearance is measured using microsomes to rank molecules in order of their metabolic stability. Although methods that use human microsomal extracts in combination with rat hepatocytes in vitro are available, they are of limited value and quite cumbersome to use. In addition, they are ineffective in their ability to discern the potential for idiosyncratic toxicity. More recently, studies based on whole genome approaches are becoming increasingly common. These suffer from the need to use high doses to invoke detectable transcriptional responses. In addition, they are unable to predict toxicity that arises due to responses that are non-transcriptional in origin. This is exemplified by perhexylline, which is known to induce steatosis but does not conform to the gene expression of another steatotic compound, amiodarone.
The pressing need is for an approach that integrates many facets of toxic pathways to the chemical structure in a systematic manner to provide detailed mechanistic rationale for toxicity as well as potential biomarkers for detection. Such an approach carries the potential to jumpstart the area of toxicity prediction which has remained stagnant without significant breakthroughs. In this article, we describe how we have developed a system approach to model pathways in the liver in silico, which can be used in combination with in vitro measurements. This can be used to create a detailed predictive platform capable of providing insights into how drugs injure the liver. Our systems approach is based on the principle that if we are able to model normal behaviour of the liver, i.e. homeostasis, we can explain toxicity as perturbations of this normal system. This approach allows us to model the biology independent of the action of any drug and allows us to design a predictive system that can generalise and is not limited by “training space”.
In the field of our disclosure, several QSAR based models are available for user-defined toxic endpoints. These are limited applications since they tend to be specific to the training-set's chemical space. Some in vitro methods provide reasonable correlations with in vivo pharmacokinetics. However, not all of them are able to address issues where large differences in properties result from simple substitutions in chemical entities. Hence, improved testing methods are necessary. Toxicogenomic methods also are being used but are unable to address situations where the toxicity is not associated with transcriptional changes.
In terms of problems our disclosure addresses, our disclosure dispenses with the notion of training space. While in the present incarnation, it focuses on the liver, it can be applied to any organ system or type of toxicity provided an appropriate steady state can be defined. Our system provides a mechanistic understanding of the toxicity and the key biochemical event(s) that is/are being perturbed.
Organ toxicity is a complex phenomenon which can be caused due to multiple biological components being affected. QSAR models cannot capture such complexity. The other main drawback of QSAR based models is that of the training set. The model's performance is only as good as the training set that it is trained on. Whenever, examples different from the training set are considered, the QSAR models run into limitations. Our disclosure dispenses with the notion of training set. The model is also a generic model of cell survival and can be applied to various organs as well with appropriate changes in the kinetics of the enzymes. Its wide applicability is due to the systems approach used whereby toxicity and other unrelated disease states can be analysed with equal facility.